Microsoft’s promise of out-of-the-box business intelligence
functionality was long since fulfilled in SQL Server 2005, but access to that
functionality has largely been confined to people who were SQL
Server-proficient. Only now, with SharePoint
, is that functionality available on the business side, free of deep
dependency on IT.

Increasingly, analytics is an ad hoc tool of decision-makers
and stakeholders. Strategic advantage is increasingly a thing of the past, and
rapid response is the greatest strength of the enterprise. Analytic decision
support that can be delivered rapidly and flexibly is an invaluable asset, and
with the new SharePoint release, Microsoft has made its best offering yet, with
a toolset that requires little or no IT intervention to be effective in
non-technical hands.

Big data on little

The catch-22 of analytics is that big data is the best fuel
for powering it – more data means better results – but working with hundreds of
millions of rows of information doesn’t happen neatly on the working manager’s
desk. The resulting trade-off, historically, has been the ability to do limited
ad hoc analytics locally with modest batches of data (via Excel), versus static
analytics from big data using custom apps or third-party packages. There’s been
almost no middle ground.

But the growing need in the marketplace has been big data
processing power for ad hoc analytic solutions. The entire point of business
analytics is to clear noise from signal in leveraging data for decision
support; what constitutes noise and signal differs in big data,
problem-to-problem. Excel is an ideal tool for ad hoc analysis, ubiquitous in
the marketplace, and not difficult to learn; but getting the right data into it
has been problematic, even via Excel
(part of SharePoint since 2007), and getting enough data into it has been flatly impossible.

Pitching big data
from the back end

The problem is two-fold on the data side: bringing data
together from different sources (particularly when some of those sources are
not on the Microsoft platform), and coping with the processing overhead that
results from continuous massive reads. SharePoint addresses both of these
problems and the 2013 release in particular opens new doors for big data in
faraway places.

OData (Open Data
Protocol) is now the primary data transport facility for accessing data sources
both foreign and domestic in a SharePoint context. Connectivity to business
data has always been available in SharePoint, but it’s been klunky and
difficult to use, and completely opaque at the business user level. The
introduction of OData greatly enhances ease-of-use, and allows the business
user to pull from relevant sources with minimal dependence on IT.

There’s more to the back-end picture, however. A common
problem with ad hoc analytics is that the primary data source is usually a
sprawling data warehouse that is neither optimized for analytics nor organized
in such a way that typical business users can dig out what they need without
substantial training.


SharePoint 2013’s leg up on this point is its PerformancePoint
implementation, which is deeper and more convenient (Kerberos delegation, for
instance, is no longer required). If the enterprise is best served by a primary
body of data, a separate repository for that data is in order, so that heavy
hits on the source will not impact non-analytical querying.

Finally, there’s the problem of getting the right data to
the right user for the right purpose. The implementation of analytics-dedicated
data sources creates an opportunity for the deployment of data models that are
specifically optimized for the analytics users in the enterprise, data models
that offer both efficiency and flexibility in ad hoc selection of information
for problem-solving. It’s true that the creation of such data models is
generally beyond the typical business user; but that’s all the more reason to
get all the consumers of analytics together to work out the most useful design
(which IT can readily implement), to ensure maximum utility for the greatest
number of users.

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